Line fault classification

ABSTRACT

This invention relates to a method of managing a digital subscriber line, where classification for a potential fault on a line is generated. The invention continuously measures the signal to noise (SNR) margin on the DSL line, and compares the measurements to predetermined conditions based on SNR margin characteristics associated with a population of good lines. Once the SNR margin measurements fail to meet the predetermined conditions, the line is flagged as experiencing a potential fault. Further measurements from weather sensors are gathered, and correlated with the SNR margin characteristics. The resulting correlation is used to generate a classification of a potential fault on the line. This can then be used to more specifically direct diagnosis and further testing.

FIELD OF THE INVENTION

This invention relates to a method of managing a digital subscriberline, in particular to classifying a fault on a digital subscriber line.

BACKGROUND TO THE INVENTION

Digital subscriber line (DSL) services, commonly referred to as“broadband” services, are deployed using metallic PSTN lines that runbetween a digital subscriber line access multiplexer (DSLAM) and modemsin subscribers' properties. With asymmetric DSL (ADSL) the DSLAM islocated in the exchange and the line can be typically up to 7 km inlength. With very-high bit-rate DSL (VDSL), the DSLAM is located in alocal cabinet with the line being much shorter, typically a maximum of 2km. The line is normally made up of a twisted copper pair, but caninclude lengths of aluminium.

Faults on DSL lines are not uncommon, and currently most faults arefound by customers reporting problems such as their line being noisy,having slower than expected broadband speed, or even interruptedbroadband service. Troubleshooting a fault often includes performingline tests on the line. Line tests can also be performed proactively toidentify faults before a customer reports them. These line tests aretypically electrical line tests that measure the electricalcharacteristics of a line and check that the results meet a standard(for example, as set out in SIN349 by British Telecommunications plc).It is also possible to compare line tests over a period of time to seeif the line's electrical characteristics are deteriorating. Once a faulthas been detected, an engineer can use electrical line testing,typically pair quality tests, to try and determine where the fault islocated and make the appropriate repairs.

However, there are a range of fault conditions which are not picked upby this process. This can be due to the faults being intermittent or notsevere enough to be measureable using existing techniques. Intermittentfaults are particularly problematic to find but can cause greatdisruption to broadband services where a line drop can result in aservice outage whilst the line retrains.

Furthermore, the deployment of PSTN lines requires joints to be made tojoin together different sections of cable. Joints are weather sealed,typically using a combination of gel filled crimp connectors and/orphysical enclosures such as joint boxes, manholes and cabinets, andinsulated from each other. However, weather sealing can fail allowingmoisture into joints causing corrosion or conductivity between pairs,commonly known as a wet joint. Insulation can also be damaged by beingabraded against physical objects such as trees, resulting in moistureaffecting the line.

International patent application WO2013/154579 describes variousdiagnostic methods for telephone lines. Patterns of line data are used,and observed data distributions are classified using modelleddistributions previously determined to correspond to known lineactivity, fault type, or fault location.

“Management of a DSL copper network using built-up qualification tools”by Martin Nilsson, Master's Degree Project, Stockholm 2005, provides anoverview of DSL systems, and introduces concepts for regular monitoringmeasurements on a line performed periodically. The data is collected ina database for further analysis. A toolbox of programs are described toput the concepts into action.

SUMMARY OF THE INVENTION

According to one aspect of the present invention, there is provided amethod of managing a digital subscriber line in a telecommunicationsnetwork, comprising:

-   -   measuring a signal to noise ratio parameter associated with the        digital subscriber line;    -   identifying variations in the signal to noise ratio parameter;    -   measuring a plurality of weather parameters associated with the        digital subscriber line;    -   comparing the signal to noise ratio parameter with one or more        predetermined conditions, wherein the predetermined conditions        are based on signal to noise ratio parameter characteristics        associated with a population of lines operating without a fault;        and if one or more of the predetermined conditions are not met,        then    -   determining a correlation between the variations in the signal        to noise ratio parameter and each of the plurality of weather        parameters;    -   generating a fault classification for the line in dependence on        the correlation.

The steps of the method may be performed repeatedly to generate aplurality of fault classifications for the line over a period of time.

Determining a correlation may comprise comparing each of the pluralityof weather parameters with a respective predetermined threshold orthresholds.

The method may further comprise allocating a specific test to the linein dependence on the fault classification.

The signal to noise ratio parameter may be the signal to noise ratiomargin.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present invention reference will nowbe made by way of example only to the accompanying drawings, in which:

FIG. 1 is a system diagram showing a telephone exchange and a DSL linerunning to a customer premises;

FIG. 2 is a flow chart illustrating the steps of an example of theinvention;

FIG. 3 is a graph showing the SNR margin for a good line over a 24 hourperiod;

FIG. 4 is a graph showing the SNR margin for a potentially faulty lineover a 24 hour period;

FIG. 5 is a flow chart illustrating the steps of a further example ofthe present invention;

FIG. 6a is a graph showing the SNR margin over a 24 hour period for anexample line;

FIG. 6b is a graph showing the leaf wetness levels over a 24 hour periodfor an example line;

FIG. 7 is a flow chart illustrating the steps of another example of thepresent invention;

FIG. 8a is a graph showing the SNR margin over a 24 hour period for anexample line;

FIG. 8b is a graph showing the leaf wetness levels and rain levels overa 24 hour period for an example line.

DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention is described herein with reference to particularexamples. The invention is not, however, limited to such examples.

This invention relates to a method of managing a digital subscriberline, where classification for a potential fault on a line is generated.The invention continuously measures the signal to noise (SNR) margin onthe DSL line, and compares the measurements to predetermined conditionsbased on SNR margin characteristics associated with a population of goodlines. Once the SNR margin measurements fail to meet the predeterminedconditions, the line is flagged as experiencing a potential fault.Further measurements from weather sensors are gathered, and correlatedwith the SNR margin characteristics. The resulting correlation is usedto generate a more refined classification of a potential fault on theline. This can then be used to more specifically direct diagnosis andfurther testing.

FIG. 1 illustrates a simplified diagram of an asymmetric digitalsubscriber line (ADSL) network 100. Some elements have been omitted forsimplicity, and conversely in some practical deployments, some elementsshown are not required.

The network 100 including a customer's premises 102 connected to atelephone exchange 104 via a telephone line 106. The telephone line 106is a twisted metallic pair made of copper or aluminium. Within thecustomer premises 102, there is a customer premises equipment CPE 108,such as a router or residential gateway, and comprises a DSL modem 110.At the exchange 104, the line 106 is connected to a digital subscriberline access multiplexer DSLAM 112. The DSLAM 112 is a network elementthat provides DSL services to connected lines and associated customerpremises. The line 106 is thus also referred to as a digital subscriberline, or DSL line, though it will be appreciated that the line can alsoprovide PSTN services. The exchange also houses electrical line testequipment 114, which can be connected to the line 106 by way ofswitching equipment in the exchange 104. Switching over to the testequipment 114 will disconnect the line from the DSLAM and thusdisconnect the line from DSL services. The electrical line testequipment can measure various parameters associated with the line,including resistances, DC voltages, and capacitance.

Between the exchange 104 and the CPE 108, the line 106 passes through anumber of connection points. The line 106 leaves the exchange and firstpasses through a Primary Cross-connection Point (PCP) 116, commonlyreferred to as a street cabinet, after it leaves the exchange 104. Thecabling between the exchange 104 and the PCP 116 is largely to be foundunderground. The line 106 then goes overhead to a joint box 118 mountedon the top of a pole. The line continues to another pole and is jointedin another box 120 further down the pole. The line continues from thereto yet another pole to an aerial Distribution Point (DP) 122. From theDP 122, the line 106 takes the form of an overhead drop wire, whichterminates at the customer premises 102 at a network terminationequipment (NTE) and onto the CPE 108 and modem 110.

Whilst only one line 106 and customer premises 102 are shown in FIG. 1,it will be appreciated that the network 100 will include other lines andassociated customer premises, which have been omitted for simplicity.Each connection point will have many lines passing through it, with thePCP 116 having the most, and the DP 122 having the fewest, as lines getrouted to alternative destinations.

Each connection point can be considered as a node or junction, as theline 106 is in practice made up of multiple sections of cable, and thejoins between cable sections housed in the connection points.

The CPE 108 further includes a control module 124. The control module124 gathers various parameters and measurements associated with the line106 via the modem 110, and processes them in accordance with theinvention as described below. The control module 124 may be implementedas a software module incorporated into the firmware of the CPE 108.

Two sensors 126 and 128 are also shown. These are weather relatedsensors (such as relative humidity, rain, or leaf wetness), and will bedescribed in more detail later. Sensor 126 is located at the site of thejoint box 118, and sensor 128 at the PCP 116. Whilst only two sensorsare shown here, more sensors can be used, and positioned elsewhere inthe network 100. The locations of the sensors are preferably at one ormore points along the line 106 that are most likely to experienceweather impact, in particular moisture ingress. This is most likely tooccur at junctions between sections of the cable that make up the line106, for example in the PCP 116 or the joint box 118. Sensors couldadditionally or alternatively be placed at other distribution points(DPs) along the line. Furthermore, each sensor can be located externallyor internally at the respective DP.

Whilst the present example shows a DSLAM 112 residing in the exchange104, the invention would still be applicable to configurations where theDSLAM is located elsewhere. For example, in a fibre to the cabinet(FTTC) arrangement, the DSLAM 112 might be located in the PCP 116, anexample of which is a roadside cabinet, which is typically locatednearer the customer premises than the exchange.

In an alternative network arrangement, DSLAM like functionality can beprovided by an MSAN (multi services access node), which also providesother capabilities such as voice. The DSLAM and MSAN are both examplesof aggregation transceiver devices, which effectively aggregate a numberof DSL lines, terminating them at a plurality of modems housed within.

An example of the present invention will now be described with referenceto the flow chart of FIG. 2.

Processing starts at step 200, with the modem 110 measuring the SNRmargin associated with the line 106. The SNR margin is the differencebetween the measured SNR and the SNR required to operate the line at aspecific synchronisation rate, where the measured SNR is aninstantaneous measure of the ratio of the received wanted DSL signalpower to unwanted interference signals or noise. For example, if theline is synchronised at 8 Mbs and needs 35 dB of SNR to operate at thisrate, and the measured SNR is 41 dB, then the SNR margin would be 6 dB.

The SNR margin is monitored every 60 seconds over 24 hour cycles, andthe results are gathered by the control module 124, which stores themfor processing. The SNR margin measurements can be gathered more or lessfrequently depending on the configuration of the modem 110 in the CPE.The downstream SNR margin is used here, as it is the service on thedownstream channel that a customer notices most, rather than theupstream channel.

FIG. 3 illustrates a graph of the (downstream) SNR margin over a 24 hourperiod for a line operating without any significant faults. The SNRmargin exhibits a diurnal pattern (which is caused by radio interferenceincreasing during the hours of darkness as a result of ionosphereeffects) causing the SNR margin to drop between 8 pm and 5 am.

The SNR margins from a large population of good lines (i.e. linesconsidered to be fault free) are analysed in this way to determine SNRmargin characteristics that are representative of the good lines. TheSNR margin measurements can be taken over a number of days and averagestaken, before being processed to identify the specific characteristicscommon to the good lines. In this example, the following characteristicsare identified:

1. The daily SNR margin variation is around 3 dB2. Maximum short term (5 minutes) SNR margin variation is around 0.5 dB3. Maximum rate of change of SNR margin is around 0.5 dB per minute

The first characteristic reflects the diurnal variation over the courseof a 24 hour period. The second characteristic reflects the gradualchange in SNR margin that can take place. The third characteristic mapsthe minute by minute variation, usually due to background noise.

These SNR margin characteristics are used to generate test conditionswith associated thresholds. Other characteristics might be identifiedand used as well or instead. The SNR margin measurements from step 200are compared against these test conditions. A line with measurementsthat do not meet at least one of the test conditions is considered notto be operating normally and to be exhibiting a potential fault, andtherefore tested with an electrical line test. In this example, based onthe above characteristics, the following test conditions and thresholdsare set:

1. Daily SNR margin variation<=4 dB2. Short term (5 minutes) SNR margin variation<=2 dB3. Rate of change of SNR margin<=2 dB per minute

In this example, these test conditions are generated in advance but canbe adjusted and updated by the network operator as required, and may bebased on further inputs. The test conditions and predeterminedthresholds are stored by the control module 124, and used in step 202.Note the thresholds are set slightly above the observed characteristicsto account for minor variations that are not indicative of a fault.

In step 202, the control module 124 compares the SNR margin measurementsagainst the test conditions. This is done after each new SNR marginmeasurement is gathered from step 200, which in this example occursevery 60 seconds. Alternatively, a different period of time can be usedbetween comparisons. However, the aim is to make the comparison usingthe current or most recent SNR margin measurements.

In step 204, if the measurements from the line 106 do pass all of thetest conditions, then processing passes back to step 200, where the SNRmargin continues to be monitored and analysed. If the measurements failany one of the test conditions, then processing passes to step 206, asthe line is deemed to be suffering from a potential fault.

FIG. 4 shows graphs of SNR margin measurements for an example line thatfails to meet the test conditions. FIG. 4 plots the (downstream) SNRmargin reported every 60 seconds. Thus, the SNR margin characteristicsof this line deviates from the target in a number of ways:

1. At 04:11 the SNR margin drops by just over 2 dB in a minute.2. Around 16:00, there are several variations in the SNR margin of justover 2 dB in a minute.3. At 21:30 the SNR margin drops by 6 dB in 1 minute.4. The SNR margin continues to fall until at 10 pm it reaches 0 andcauses a line re-sync, with periods where the short term SNR marginvariation is also greater than 2 dB over 5 minutes.

Furthermore, the line actually re-syncs at a lower rate which is almost1 Mbps lower than before but restores the SNR margin to the target 9 dB.

Thus, if testing on this line begins at 00:00 with step 200, the linewould meet the test conditions during the first 4 hours or so, with theSNR margin on the line continuously being monitored and tested. Then at04:11, as the SNR margin drops by over 2 dB in a minute, the testcondition for the rate of change of the SNR margin being <=2 dB perminute will not be met. Hence processing would pass onto step 206.

The line would then meet the test conditions until around 16:00. Ataround 16:00, the SNR margin drops by over 2 dB in a minute, and so thesame test condition for the rate of change per minute is not met, andprocessing would then pass onto step 206.

At 21:30, the test for the rate of change per minute of SNR margin isnot met, and soon after the test for the short term (5 minutes) SNRmargin variation is also not met. And thus processing would also pass tostep 206 at this point.

So in step 204, if the measurements fail at least one of the testconditions, as shown at certain times in the example illustrated in FIG.4, then a potential fault is occurring and processing passes to step206. SNR margin measurements that do not meet the test conditions can bereferred to as SNR perturbations. In this example, it is the variationsin the SNR margin measurements over a given period of time that arecompared against the test conditions, which themselves are characterisedover a period of time.

In step 206, the control module 124 flags a fault for the line 106, andan electrical line test needs to be triggered immediately to capture theinstant the line is experiencing problems. In practical systems it isnot ideal to run line tests too frequently, hence a mechanism can beimplemented to set a minimum time between the line tests, which thenetwork operator can adjust as required. In this example, a timer isused (counting down from a pre-set value) by the control module 124 toachieve this, but other methods could be used to delay the line test bythe minimum time from the previous line test. Thus, in step 208, thecontrol module 124 determines if the minimum time between tests haselapsed. If it has, then processing passes directly to step 210 totrigger an electrical line test. If the minimum time has not elapsed,then the control module 124 waits until the minimum time has elapsed(step 209) before passing to step 210.

In step 210, an electrical line test is triggered. This can be done bythe control unit 124 using an API running on a server on the network,which in turn can trigger the line test equipment 114. Alternatively,the control unit 124 can be configured to directly instruct the linetest equipment 114 to perform the line test. The electrical line testsmeasure various parameters associated with the line, such as DC voltageA to earth, DC voltage B to earth, DC voltage A to B, capacitance A toearth, capacitance B to earth, capacitance A to B, resistance A toearth, and so on.

The results are analysed in step 212. Some faults can be identifieddirectly from analysis of parameters from the line test (using rangesand threshold values associated with known faults for example), thoughthe specifics of this analysis and identification are not discussedhere. Otherwise, other activities can be performed. For example, furtherfault events can be captured using the above method over a period oftime, to build up a more detailed picture, which may result in improveddiagnosis and an assessment on the impact to the DSL service of thefault level of service interruption. Alternatively, the fault event canbe correlated with other information. Processing cycles back to step 200after the line test is triggered in step 210.

The pseudocode below sets out in a simplified manner how the testconditions described above might be implemented:

-   -   IF max(SNR_(t1-t1440))−min(SNR_(t1-t1440))>4 OR        -   |(SNR_(t)−SNR_(t-5))|>2 OR        -   |(SNR_(t)−SNR_(t-1))|>2    -   THEN SET TRIGGER_TEST=TRUE        max(SNR_(t1-t1440))−min(SNR_(t1-t1440)) tests for the diurnal        variation characteristic, |(SNR_(t)SNR_(t-5))|>2 for the short        term (5 mins) SNR variation characteristic, and        |(SNR_(t)−SNR_(t-1))|>2 for the per minute SNR margin variation        characteristics.

The act of correlating with other information forms the basis of animprovement to the line testing method described above—weather optimisedline test triggering.

Atmospheric conditions can affect the operation of a DSL line. However,weather is dynamic and it is not always obvious that intermittent faultsare being caused by weather. To assess whether a fault is correlated tothe weather it is necessary to have nearby weather data available, thespatial distance and sample frequency will directly affect thecorrelation results.

The network 100 in FIG. 1 shows two sensors 126 and 128. These are usedto assess the atmospheric moisture or wetness levels, as one particularproblem that is encountered is water ingress on a line. Examples of thetypes of sensors that can be used include:

1. Relative Humidity Sensor: This measures relative humidity. When at100%, the air is fully saturated and it is possible that water maycondense to form a mist/fog but is dependent on other factors such aswind.2. Rain Sensor: A measure of rain falling over a period of time.3. Dew point: not a sensor as such, but a parameter based on knownrelationship a between temperature, relative humidity, and barometricpressure. If the temperature approaches the dew point then it is likelythat water will condense on cold objects4. Leaf Wetness: Found to be the best single sensor in studies. Usuallydeployed in the agricultural industry, seehttp://en.wikipedia.org/wiki/Leaf wetness, a leaf wetness sensormeasures the amount of dew or precipitation on a surface. A typical leafwetness sensor operates by measuring the change in electrical resistancebetween two metal conductors in an alternate finger configuration on aflat surface. Tests have found that a leaf wetness sensor provides agood indication of the moisture on PSTN and DSL circuits.

The improved approach looks to correlate the atmospheric moisturelevels, measured by one (or more) of sensors, with the SNR marginmeasurements on the line. If there is a correlation between the moisturelevels and the SNR margin measurements, then the line is classified ashaving a weather related fault. A line test is triggered at a time whenthe SNR measurements fail to meet the test conditions described above,and whilst there is a correlation with the measured atmospheric moisturelevels. The results of the line test in such a situation, together withthe knowledge that the line may be experiencing a weather related fault,can lead to more focussed and accurate fault diagnosis.

The specific steps of operation of this improved method will now bedescribed with reference to FIG. 5. The improved method is based on themain method described above, and summarised in FIG. 2, but includes someadditional features that take into account an atmospheric moisturemeasure.

Starting at step 500, the SNR margin measurement associated with theline 106 is gathered by the control module 124 from the modem 110. Anatmospheric moisture measure is also collected from one of the sensors126 and 128. Several sensors can be used, and the measurements averaged.In this example, a leaf wetness sensor is used, with the resulting leafwetness measurements calibrated from 0 to 15, with 0 indicating dry and15 indicated 100% saturated or wet. The leaf wetness measurements aregathered and stored by the control module 124.

In step 502, the resulting SNR margin measurements are analysed andcompared by the control module 124 against the test conditions. The testconditions used in this example are the same as those described above.Also as before, the comparison is done after each new SNR marginmeasurement is gathered from step 500, which in this example occursevery 60 seconds, though other intervals can be used.

In step 504, if the SNR margin measurements from the line 106 do meetthe test conditions, then processing passes back to step 500, where theSNR margin continues to be monitored and analysed every 60 seconds.Again, in this example, it is really the variations in the SNR marginmeasurements over a given period of time that are compared against thetest conditions, which themselves are characterised over a period oftime.

If the SNR measurements from the line 106 do not meet the testconditions, then a potential fault might be occurring and processingpasses to step 505 to determine whether there is a correlation betweenthe SNR variations and the leaf wetness measurements.

In step 505, the moisture level measurements given by the leaf wetnesssensors are analysed. The test is to see if the atmospheric moisturelevels meet a certain threshold level—in this example, the threshold isset to the atmospheric moisture levels being high, at 100% saturation,or when leaf wetness measure of 15. Alternatively, the threshold couldsimply be set to anything non-zero/not dry. In this example, if themoisture levels are high (i.e. leaf wetness is at 15), then the line isclassified as having a wet joint fault, and a line test should betriggered by moving to step 506. Otherwise processing passes back tostep 500, and monitoring and analysis continue at regular intervals.Furthermore, the triggering of the line test should only occur whilstthe line is experiencing a period of wetness (set at 100% saturation inthis example) as identified by the leaf wetness measure.

In step 506, the control module 124 flags a wet joint fault for the line106, and an electrical line test is triggered immediately to capture theinstant the line is experiencing problems. However, and as describedabove, it is not ideal to run a line tests too frequently. Thus, in step508, the control module 124 determines if the minimum time between testshas elapsed (see step 208), and if it has, then processing passesdirectly to step 510 to trigger an electrical line test. If the minimumtime has not elapsed, then the control module 124 waits until theminimum time has elapsed (step 509) before passing to step 510.

The line test triggered in step 510 and subsequent analysis in step 512is as per steps 210 and 212 respectively of FIG. 2.

There now follows a worked example. FIG. 6a shows a graph of SNR marginmeasurements over a 24 hour period for an example line, and FIG. 6b theleaf wetness measurements for the same line. It can be seen from FIG. 6a, that the SNR margin drops a little at around 07:00, when there is alsoa marginal leaf wetness measure. Then starting around 16:30, the SNRmargin varies significantly, dropping progressively lower until 20:00when the line resynchronises to a lower rate. The leaf wetness measurefrom around 16:30 moves from dry (0) to saturated (15) very quickly.

Applying the method described in FIG. 5 starting at 00:00, there is aninitial period up to around 07:00, where the SNR margin measurementsmeet the test conditions, and thus steps 500 to 504 are repeated, withthe SNR measurements being continuously monitored. Then at around 07:00,the SNR measurements gathered will just fail to meet the test conditionsat step 504, as the short term SNR margin variation is slightly morethan 2 dB over a 5 minute period, and processing would pass to step 505.However, at this stage, the leaf wetness is only at 1, and so themoisture levels are not high enough to pass the test in step 505. Thus,no line test is triggered, and instead processing passes back to step500, and the line continues to be monitored. Then when we arrive ataround 16:30, the SNR variations are significant enough that the testconditions in step 504 are not met again. However, this time themoisture levels are high, with the leaf wetness sensor returning 15.Thus, processing passes to step 506, with the line flagged asexperiencing a wet joint fault, and a line test triggered. After thispoint, the SNR margin variations remain significant for the next 4 hoursor so, whilst the leaf wetness also remains high at 15, so line testswill be triggered regularly, at a rate capped by the minimum timebetween tests in step 508.

Note, in this example line in FIG. 6, applying the initial method ofFIG. 2 that only takes into account SNR variations, a line test wouldhave been triggered around 07:00, as well at 16:30 and after that.However, with the improved method of FIG. 5 that takes into accountmoisture level measurements, a line test is only trigger from 16:30.Thus, line testing is directed in a more controlled manner to when thereis likely to be a weather related problem.

In a further improvement to the line testing method described above inrelation to FIG. 5 using weather sensors, multiple sensors are used togenerate a more refined classification of a potential fault on the line.This can then be used to more specifically direct diagnosis and furthertesting.

In this further improvement, a range of sensors are used. For example,both a leaf wetness sensor and a rain sensor (of the type describedabove) are used. The sensors are placed at the same point in the networksomewhere along or near the line 106 (such as both at the PCP 116), ormay be placed separately from each other (such as one at the PCP 116 andthe other on the pole 118). FIG. 7 is a flow chart illustrating thesteps of this further improved method. In step 700, the SNR marginmeasurements are gathered from the line 106, as well as measurementsfrom these two sensors.

In step 702 (as in step 502), the resulting SNR margin measurements areanalysed and compared by the control module 124 against the testconditions. In step 704 (as in step 504), if the SNR margin measurementsfrom the line 106 do meet the test conditions, then processing passesback to step 700, where the SNR margin continues to be monitored andanalysed repeatedly.

If the SNR measurements from the line 106 do not meet the testconditions, then a potential fault might be occurring and processingpasses to step 706.

In step 706, the fault is assessed by determining the correlation, ifany, between the SNR margin variations and the moisture levelmeasurements. Using the resulting correlation results, a refinedclassification is generated for the fault. The correlation method isillustrated here with reference to an example line.

FIG. 8a shows a graph of SNR margin over a 24 hour period for an exampleline, and FIG. 8b the moisture measurements for the line from the twosensors—leaf wetness levels and rain levels. In a first period between00:00 and 06:30, the SNR margin shows minor variations, during whichtime the leaf wetness is also generally high. However, there is no rainmeasured during this period. In a second period at 21:00 to 22:00, theSNR margin shows a major variation and drops significantly, so much sothat the DSL line actually resynchronises, during which time the leafwetness levels are high, and the rain levels are also high during thesame period.

Therefore, during the first period, it can be seen that there is acorrelation between the SNR margin variations and the leaf wetnesslevels, but not the rain levels. The correlation can be determined invarious ways. One approach is to examine the measurements from theweather sensors (when the SNR margin variations do not meet the testconditions), and comparing the measurements to thresholds or ranges. Forexample, the leaf wetness sensor measurements can have associatedthresholds of =0 and >0 to represent dry (no correlation) and damp(correlation) respectively. The rain sensor measurements can have anassociated thresholds=0 mm and >0 mm to represent dry (no correlation)and raining (correlation). Thus, using these thresholds for the firstperiod, it is clear that there is only correlation with leaf wetness,but not rain. However, during the second period there is a correlationbetween both the leaf wetness levels and the rain levels.

The total correlation thus depends on the various measurements at aspecific point in time. In this example, in the first period there isgood correlation between the SNR margin variations and the leaf wetnesslevels, but in the second period there is good correlation between theSNR margin variations and both the leaf wetness levels and rain levels.The degree of correlation between the SNR margin variations and thenumber of moisture level measurements is used to classify the line instep 708.

In step 708, the line 106 can be assigned a line fault classificationthat reflects the correlation. One example of classifications is shownbelow:

1. Line has SNR perturbations only (Class 1)2. Line has SNR perturbations and wetness correlation (Class 2)3. Line has SNR perturbations, and wetness correlation and raincorrelation (Class 3)

The method operates continuously by cycling back to step 700, and thusthe generated line fault classification is updated continuously. Thus,the method will classify the line in FIG. 8 with a class 2 fault up toaround 06:30, and then around 16:00 the line will be classified with aclass 1 fault, as only SNR perturbations are measured, but between 21:00and 22:00, the line will be classed with a class 3 fault.

Alternatively, each correlation (of say wetness or rain) can result inan individual correlation score of 1, and the final correlation (orclassification) is the sum of the individual correlation scores. Forexample, if SNR perturbations do not correlate with any of the moisturelevel measurements, then the individual correlations scores are 0, andthe final correlation is 0. But if just one of either of moisture levelmeasurements result in correlation with the SNR perturbations, then asingle individual correlation score of 1 results, so the finalcorrelation is 1. If both moisture level measurements correlate with theSNR perturbations, then two individual correlation scores result, whichwill result in a final correlation of 2. This approach gives scope forfurther moisture level measures or sensors to be used (e.g. a dew-pointsensor), with the possibility of higher degrees of final correlation.

The resulting classification can be used to trigger a suitable action instep 710. For example, electrical line tests as described earlier can beset to only trigger for faults that have a certain classification. Morespecific testing or further an engineer visit can be allocated to linesthat have classifications based on high correlations, which might bemore problematic or difficult to resolve.

Whilst the example described above focus on atmospheric moisture levels,other more general weather related measures could be used instead, suchas wind speed, which could help ascertain whether driving rain is afactor, or if a line is rubbing against an object in high wind.Thresholds for wind speed can be set accordingly to determinecorrelation, such as corresponding to zero wind, low wind, and highwind.

Whilst the examples refer to SNR margin measurements, a person skilledin the art will appreciate that an absolute measure of the SNRassociated with the line can be used instead an alternative indicator ofthe performance of the line.

Exemplary embodiments of the invention are realised, at least in part,by executable computer program code which may be embodied in anapplication program data. When such computer program code is loaded intothe memory of a processor in the control module 124, it provides acomputer program code structure which is capable of performing at leastpart of the methods in accordance with the above described exemplaryembodiments of the invention.

A person skilled in the art will appreciate that the computer programstructure referred can correspond to the flow chart shown in FIG. 2,where each step of the flow chart can correspond to at least one line ofcomputer program code and that such, in combination with the processorin the control module 124, provides apparatus for effecting thedescribed process. In a similar manner, computer program structure forthe alternative methods can correspond to the flow charts shown in FIG.5 or FIG. 7.

In general, it is noted herein that while the above describes examplesof the invention, there are several variations and modifications whichmay be made to the described examples without departing from the scopeof the present invention as defined in the appended claims. One skilledin the art will recognise modifications to the described examples.

1. A method of managing a digital subscriber line in atelecommunications network, comprising: measuring a signal to noiseratio parameter associated with the digital subscriber line; identifyingvariations in the signal to noise ratio parameter; measuring a pluralityof weather parameters associated with the digital subscriber line;comparing the signal to noise ratio parameter with one or morepredetermined conditions, wherein the predetermined conditions are basedon signal to noise ratio parameter characteristics associated with apopulation of lines operating without a fault; and if one or more of thepredetermined conditions are not met, then determining a correlationbetween the variations in the signal to noise ratio parameter and eachof the plurality of weather parameters; generating a faultclassification for the line in dependence on the correlation.
 2. Amethod according to claim 1, wherein the steps of the method areperformed repeatedly to generate a plurality of fault classificationsfor the line over a period of time.
 3. A method according to claim 1,wherein determining a correlation comprises comparing each of theplurality of weather parameters with a respective predeterminedthreshold or thresholds.
 4. A method according to claim 1, wherein themethod further comprises allocating specific testing to the line independence on the fault classification.
 5. A method according to claim1, wherein the signal to noise ratio parameter is the signal to noiseratio margin.